Generating visibly and physically plausible images is an important task for the users to get more in-depth understanding of the data. In order for computers to generate such images, light material interactions have to be considered. Those interactions are computed through the mathematical and physical foundations set by the light transport algorithms. Because it is not possible to compute all the interactions, different mathematical approaches are used by conventional technologies.
One popular approach for computing light interactions is the use of Monte Carlo (MC) statistical methods. Complicated phenomena can be computed with MC methods but the artifacts of these algorithms appear as noise in the resulting images. The square of number of samples in the algorithm can only improve the results linearly and that costs as computation time.
MC statistical methods explore the space using statistical light transport events and directions. These directions are not known ahead and, generally, the directions are selected according to the events. The energy carrying paths providing the most contributions aids in faster convergence of the resulting image to a non-noisy image. If these paths were known ahead of time, the points which contribute most can be addressed easily and noise free images can be acquired. The problem is to represent those paths because each path is a function of begin, end points, the scattering functions on each end and the visibility value between those two ends. This problem can be simplified if the scattering functions are chosen as isotropic scattering functions. The problem then can be represented as a matrix of points and the visibility between those points. This matrix is referred to herein as the light transport matrix.
The light transport matrix is a very large matrix; its size is defined by all possible combinations of points and their relations. This large size makes tasks such as processing and storage unwieldy from a computation perspective. This limits the ability to efficiently utilize the light transport matrix in applications such as medical imaging applications. Accordingly, it is desired to provide techniques for light transport matrix computation that result in a reduced overall size of the matrix without losing the fidelity (and, thus, usefulness) of the data in the matrix.